The paper extends Co‑Scientist, a Gemini‑based multi‑agent system, and validates it in real‑world scientific settings. In materials science it designed a safe precursor route for MXenes and achieved single‑attempt growth of monolayer MoS₂, MoSe₂, and WS₂. In biology it predicted swarming phenotypes of engineered E. coli, and in computer science it discovered a superior inference‑time scaling architecture for HealthBench. A double‑blind study with 30 experts showed that Co‑Scientist’s reliability modules reduce hallucination and plagiarism while improving research safety.
arXiv:2607.03863v3 Announce Type: replace Abstract: Artificial intelligence has advanced scientific discovery, but most AI4Science systems remain fragmented tools that rely on humans to coordinate pr...
PAPERBENCHX is a new benchmark that tests whether AI can reproduce scientific experiments across 12 disciplines, including electromagnetics, photonics, chemistry, materials science, biology, and robotics. It consists of 93 expert-constructed tasks, each providing a source paper, a public specification, and a domain-native scientific environment. The benchmark evaluates AI agents by having them reconstruct, execute, and diagnose key experiments, then replay submissions from scratch to score regenerated evidence against expert-validated criteria, revealing that current AI configurations fully reproduce only 14% of tasks.
arXiv:2502. 18864v2 Announce Type: replace Abstract: Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation.
MatOS is an agent operating system designed to streamline materials research by organizing data, knowledge, tools, and workflows into reusable assets. It features a layered scientific asset graph, a stateful adaptive workflow that tracks and resumes tasks, and an evidence-driven asset evolution mechanism that updates assets based on execution records and user feedback. In benchmark tests, MatOS outperforms existing agents and has successfully narrowed millions of candidate structures to a few promising solid-electrolyte and halide variants, demonstrating its effectiveness in long, multistage materials studies.
The paper introduces ScientistTwo, a fully autonomous multi‑agent framework that takes a scientific problem, establishes baselines, generates hypotheses, and coordinates specialized agents to conduct an end‑to‑end discovery cycle without human intervention. It rigorously tests and refines its methods through automated experiments, ablation studies, and a closed‑loop peer‑review engine. Benchmarking against top conferences (ICLR, ICML, NeurIPS) shows that ScientistTwo produces expert‑level, publishable papers and codebases that outperform human state‑of‑the‑art models and receive higher review ratings under automated AI review.
arXiv:2608. 06961v1 Announce Type: new Abstract: Early-stage molecular design is an iterative process, not just a task of generating molecules.
MoLRSI is a recursive self‑improving AI agent designed for molecular discovery that optimizes both workflow and model architecture using persistent cross‑task memory. It refines its search policy based on workflow performance, refinement outcomes, and execution feedback, enabling continuous improvement. In antibiotic discovery, MoLRSI achieved a 15.9% experimental hit rate versus 8.7% for an expert‑led workflow, and in de novo peptide‑MHC binder design it produced eight validated hits with preferential binding, outperforming a baseline approach.
arXiv:2607. 02329v1 Announce Type: new Abstract: Autonomous-research agents have demonstrated end-to-end LLM automation in machine-learning sandboxes where execution provides calibration.
arXiv:2608. 02642v1 Announce Type: cross Abstract: Accelerating scientific discovery is among the most consequential applications of AI, and computational biomolecular simulation stands out as a particularly promising target within this broader effort.
arXiv:2601. 13508v4 Announce Type: replace-cross Abstract: Autonomous agents are beginning to transform scientific research from tool-assisted workflows toward self-sustaining discovery processes.
arXiv:2606. 01316v1 Announce Type: new Abstract: Scientific discovery demands intelligence, perseverance, and serendipity across vast search spaces.
arXiv:2608. 11224v1 Announce Type: new Abstract: Materials research advances through accumulated experience - scripts that work, protocols that are trusted, warnings attached to failed calculations or experiments, and judgement that links a new question to an old result.